For metastatic castration resistant prostate cancer, recent advances have led to the deployment of second- generation ADT (ADT2) therapies, including abiraterone acetate (AA), which targets a component of androgen biosynthesis, and enzalutamide, which targets the androgen receptor directly. Both AA and enzalutamide have demonstrated an overall survival benefit in patients with metastatic CRPC; however, most patients still develop resistance to these agents, which drives prostate cancer-associated morbidity and mortality. Several mechanisms of resistance to ADT2 have recently been identified, although the overall spectrum of resistance mechanisms to ADT2 remains incompletely characterized, as does the biological impact of these events. Moreover, the extent to which such mechanisms might generalize across ADT2 regimens or operate in specific therapeutic contexts remains unknown. Finally, subsequent treatment options for this patient population beyond the use of cytotoxic chemotherapies (e.g. taxanes) are not well defined. The goal of this proposal is to create and apply computational biology algorithms that 1) systematically interrogate genomic resistance effectors to ADT2 in clinically relevant time points, 2) integrate in vitro models of ADT2 resistance with genomic features to define biological modules germane to ADT2 resistance, and 3) model clinical resistance with genomic data to inform subsequent treatment strategies. In doing so, we aim to discover new modules for clinical ADT2 resistance, provide insight into the expansion of rational treatment approaches for ADT2-resistant patients, and create an inferential framework through which clinicians may ultimately predict those treatment strategies most likely to benefit individual patients based on tumor genomic profiles. These efforts will facilitate a focused and comprehensive assessment of ADT2 resistance in the neoadjuvant and metastatic CRPC settings, explain genetic resistance to ADT2 in prostate cancer, and define subsequent therapeutic strategies.